Full-Time

Senior Applied AI Engineer

LTS

LTS

201-500 employees

Builds, deploys, sustains mission-critical federal IT

No salary listed

Remote in USA

Remote

Bachelor's

Category
AI & Machine Learning (1)
Required Skills
LLM
Python
RAG
LangGraph
REST APIs
LangChain

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Requirements
  • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional experience).
  • 5+ years of software engineering, applied AI, machine learning, or AI systems development experience.
  • Demonstrated experience developing production AI applications powered by Large Language Models (LLMs).
  • Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems.
  • Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval.
  • Experience evaluating AI model performance and implementing experimentation frameworks.
  • Strong programming skills in Python and experience with modern software engineering practices.
  • Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • Experience with using AI coding assistants as part of your daily workflow.
  • Familiarity with REST APIs, cloud-native applications, and distributed software systems.
  • Strong analytical, problem-solving, and communication skills.
  • Intellect and curiosity for AI systems and how they behave.
  • Deep passion for experimenting with new AI techniques.
  • Background in evaluation, explainability, and continuous improvement.
  • Proven success with ownership of difficult technical challenges and collaboration across disciplines.
Responsibilities
  • Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity.
  • Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement.
  • Rapidly prototype new AI capabilities and transition successful experiments into production.
  • Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management.
  • Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques.
  • Improve AI response quality through experimentation, benchmarking, and iterative optimization.
  • Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness.
  • Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems.
  • Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement.
  • Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management.
  • Design approaches that maximize retrieval quality across large technical documentation and source code repositories.
  • Improve how AI agents discover, organize, and reason over enterprise knowledge.
  • Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform.
  • Share research findings, experimental results, and engineering recommendations with cross-functional teams.
  • Help establish best practices for experimentation, evaluation, and AI quality throughout the organization.
Desired Qualifications
  • Experience optimizing autonomous or multi-agent AI systems.
  • Experience implementing automated AI evaluation frameworks.
  • Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Vertex AI, AWS Bedrock, or open-source LLMs.
  • Experience with vector databases including Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search.
  • Experience with Responsible AI, AI governance, safety, and explainability.
  • Familiarity with software engineering tools, code intelligence platforms, or developer productivity solutions.
  • Experience supporting healthcare, Federal Government, or other highly regulated environments.
  • Experience using AI coding assistants and autonomous agents as part of daily software development.

LTS provides both consulting and hands-on implementation for mission-critical needs in government and health, including disaster response, healthcare kiosks, occupational health, and federal IT work. Its approach combines program management, systems integration, and operations support to design, build, deploy, and sustain infrastructure and programs in the field. The company differentiates itself with end-to-end delivery across the full project lifecycle for government and health contexts, backed by experience across federal, state, local, and tribal levels and an emphasis on regulatory and security requirements. Its goal is to turn plans into durable, operational programs that keep communities safe and healthy.

Company Size

201-500

Company Stage

N/A

Total Funding

N/A

Headquarters

Herndon, Virginia

Founded

2005

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Simplify Jobs

Simplify's Take

What believers are saying

  • Lindsay Goldberg grew LTS revenue and EBITDA over 150% from 2020 to 2025.
  • Fifteen acquisitions and all-50-state coverage give Velocity immediate national scale.
  • Rail, renewable diesel, and generator fueling deepen cross-sell into mission-critical customers.

What critics are saying

  • Wind Point merged LTS into Velocity Rail in July 2025, eliminating standalone independence.
  • Commodity fuel delivery faces margin pressure if customers insource fueling or switch distributors.
  • Integration across 18,000 customers and 30,000 sites risks service failures and churn.

What makes LTS unique

  • 1998-founded LTS became North America's largest mobile on-site refueling operator by 2025.
  • LTS serves trucking, rail, marine, and emergency power without branch depots.
  • Its truck-to-truck model and 1,200 specialized vehicles create dense route economics.

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Benefits

Remote Work Options

Flexible Work Hours

Company News

Benzinga
Jul 22nd, 2025
LTS Sold to Velocity Rail Solutions

Lindsay Goldberg has completed the sale of Liquid Tech Solutions (LTS) to Velocity Rail Solutions. Under Lindsay Goldberg's ownership since 2020, LTS grew revenue and EBITDA by over 150%, expanded to all 50 states, and executed 15 strategic acquisitions. The transaction terms were not disclosed. Financial advisors included Harris Williams, UBS Investment Bank, and Citizens Capital Markets & Advisory.